Overview

Dataset statistics

Number of variables23
Number of observations52
Missing cells561
Missing cells (%)46.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.5 KiB
Average record size in memory186.5 B

Variable types

Numeric18
Unsupported4
Categorical1

Alerts

Sugarcane and products has constant value "0.0"Constant
Year is highly overall correlated with Natural gas and 8 other fieldsHigh correlation
Oil is highly overall correlated with Coal and 4 other fieldsHigh correlation
Natural gas is highly overall correlated with Year and 7 other fieldsHigh correlation
Coal is highly overall correlated with Year and 10 other fieldsHigh correlation
Total Primaries is highly overall correlated with Oil and 1 other fieldsHigh correlation
Electricity is highly overall correlated with Year and 7 other fieldsHigh correlation
LPG is highly overall correlated with Coal and 3 other fieldsHigh correlation
Kerosene/jet fuel is highly overall correlated with Non-energyHigh correlation
Diesel oil is highly overall correlated with LPG and 1 other fieldsHigh correlation
Fuel oil is highly overall correlated with Diesel oilHigh correlation
Coke is highly overall correlated with Year and 6 other fieldsHigh correlation
Charcoal is highly overall correlated with Year and 8 other fieldsHigh correlation
Gases is highly overall correlated with Natural gas and 3 other fieldsHigh correlation
Other secondary is highly overall correlated with Year and 6 other fieldsHigh correlation
Non-energy is highly overall correlated with Year and 8 other fieldsHigh correlation
Total Secundaries is highly overall correlated with Year and 8 other fieldsHigh correlation
Total is highly overall correlated with Year and 7 other fieldsHigh correlation
Oil has 26 (50.0%) missing valuesMissing
Natural gas has 24 (46.2%) missing valuesMissing
Coal has 6 (11.5%) missing valuesMissing
Hydroenergy has 52 (100.0%) missing valuesMissing
Nuclear has 52 (100.0%) missing valuesMissing
Firewood has 52 (100.0%) missing valuesMissing
Sugarcane and products has 47 (90.4%) missing valuesMissing
Other Primary_x000d_ has 52 (100.0%) missing valuesMissing
Total Primaries has 2 (3.8%) missing valuesMissing
LPG has 38 (73.1%) missing valuesMissing
Kerosene/jet fuel has 45 (86.5%) missing valuesMissing
Diesel oil has 40 (76.9%) missing valuesMissing
Fuel oil has 40 (76.9%) missing valuesMissing
Coke has 4 (7.7%) missing valuesMissing
Charcoal has 1 (1.9%) missing valuesMissing
Gases has 15 (28.8%) missing valuesMissing
Other secondary has 28 (53.8%) missing valuesMissing
Non-energy has 37 (71.2%) missing valuesMissing
Year is uniformly distributedUniform
Year has unique valuesUnique
Electricity has unique valuesUnique
Total Secundaries has unique valuesUnique
Total has unique valuesUnique
Hydroenergy is an unsupported type, check if it needs cleaning or further analysisUnsupported
Nuclear is an unsupported type, check if it needs cleaning or further analysisUnsupported
Firewood is an unsupported type, check if it needs cleaning or further analysisUnsupported
Other Primary_x000d_ is an unsupported type, check if it needs cleaning or further analysisUnsupported
Gases has 1 (1.9%) zerosZeros

Reproduction

Analysis started2023-07-30 07:43:41.904368
Analysis finished2023-07-30 07:45:04.395054
Duration1 minute and 22.49 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Year
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1995.5
Minimum1970
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:45:04.543295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1970
5-th percentile1972.55
Q11982.75
median1995.5
Q32008.25
95-th percentile2018.45
Maximum2021
Range51
Interquartile range (IQR)25.5

Descriptive statistics

Standard deviation15.154757
Coefficient of variation (CV)0.0075944662
Kurtosis-1.2
Mean1995.5
Median Absolute Deviation (MAD)13
Skewness0
Sum103766
Variance229.66667
MonotonicityStrictly increasing
2023-07-30T07:45:04.814150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1970 1
 
1.9%
1971 1
 
1.9%
1998 1
 
1.9%
1999 1
 
1.9%
2000 1
 
1.9%
2001 1
 
1.9%
2002 1
 
1.9%
2003 1
 
1.9%
2004 1
 
1.9%
2005 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
1970 1
1.9%
1971 1
1.9%
1972 1
1.9%
1973 1
1.9%
1974 1
1.9%
1975 1
1.9%
1976 1
1.9%
1977 1
1.9%
1978 1
1.9%
1979 1
1.9%
ValueCountFrequency (%)
2021 1
1.9%
2020 1
1.9%
2019 1
1.9%
2018 1
1.9%
2017 1
1.9%
2016 1
1.9%
2015 1
1.9%
2014 1
1.9%
2013 1
1.9%
2012 1
1.9%

Oil
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct26
Distinct (%)100.0%
Missing26
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean202.66231
Minimum0.04
Maximum336.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:45:05.053124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.04
5-th percentile41.81
Q1129.4525
median240.575
Q3275.9475
95-th percentile314.5
Maximum336.25
Range336.21
Interquartile range (IQR)146.495

Descriptive statistics

Standard deviation99.1504
Coefficient of variation (CV)0.48923947
Kurtosis-0.91431756
Mean202.66231
Median Absolute Deviation (MAD)52.2
Skewness-0.65919191
Sum5269.22
Variance9830.8018
MonotonicityNot monotonic
2023-07-30T07:45:05.270537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
127.54 1
 
1.9%
275.64 1
 
1.9%
72.63 1
 
1.9%
54.47 1
 
1.9%
80.68 1
 
1.9%
37.59 1
 
1.9%
336.25 1
 
1.9%
64.81 1
 
1.9%
318.27 1
 
1.9%
303.19 1
 
1.9%
Other values (16) 16
30.8%
(Missing) 26
50.0%
ValueCountFrequency (%)
0.04 1
1.9%
37.59 1
1.9%
54.47 1
1.9%
64.81 1
1.9%
72.63 1
1.9%
80.68 1
1.9%
127.54 1
1.9%
135.19 1
1.9%
160.92 1
1.9%
192.59 1
1.9%
ValueCountFrequency (%)
336.25 1
1.9%
318.27 1
1.9%
303.19 1
1.9%
296.99 1
1.9%
283.06 1
1.9%
278.05 1
1.9%
276.05 1
1.9%
275.64 1
1.9%
271.67 1
1.9%
268.27 1
1.9%

Natural gas
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct28
Distinct (%)100.0%
Missing24
Missing (%)46.2%
Infinite0
Infinite (%)0.0%
Mean258.365
Minimum50.09
Maximum463.29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:45:05.505042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum50.09
5-th percentile74.0185
Q1124.8075
median249.92
Q3389.8375
95-th percentile448.4265
Maximum463.29
Range413.2
Interquartile range (IQR)265.03

Descriptive statistics

Standard deviation137.4872
Coefficient of variation (CV)0.53214328
Kurtosis-1.6070805
Mean258.365
Median Absolute Deviation (MAD)134.85
Skewness0.014100355
Sum7234.22
Variance18902.73
MonotonicityNot monotonic
2023-07-30T07:45:05.709105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
132.84 1
 
1.9%
406.85 1
 
1.9%
398.41 1
 
1.9%
386.98 1
 
1.9%
384.32 1
 
1.9%
385.56 1
 
1.9%
463.29 1
 
1.9%
457.25 1
 
1.9%
402.35 1
 
1.9%
335.26 1
 
1.9%
Other values (18) 18
34.6%
(Missing) 24
46.2%
ValueCountFrequency (%)
50.09 1
1.9%
70.9 1
1.9%
79.81 1
1.9%
107.43 1
1.9%
114.62 1
1.9%
118.49 1
1.9%
121.11 1
1.9%
126.04 1
1.9%
132.84 1
1.9%
139.54 1
1.9%
ValueCountFrequency (%)
463.29 1
1.9%
457.25 1
1.9%
432.04 1
1.9%
408.78 1
1.9%
406.85 1
1.9%
402.35 1
1.9%
398.41 1
1.9%
386.98 1
1.9%
385.56 1
1.9%
384.32 1
1.9%

Coal
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct45
Distinct (%)97.8%
Missing6
Missing (%)11.5%
Infinite0
Infinite (%)0.0%
Mean105.52065
Minimum1.14
Maximum562.35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:45:05.932470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.14
5-th percentile10.925
Q137.9425
median81.935
Q3147.045
95-th percentile274.725
Maximum562.35
Range561.21
Interquartile range (IQR)109.1025

Descriptive statistics

Standard deviation100.84414
Coefficient of variation (CV)0.95568151
Kurtosis8.5172128
Mean105.52065
Median Absolute Deviation (MAD)49.865
Skewness2.4029973
Sum4853.95
Variance10169.54
MonotonicityNot monotonic
2023-07-30T07:45:06.178884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
17.9 2
 
3.8%
96.77 1
 
1.9%
107.52 1
 
1.9%
108.6 1
 
1.9%
35.38 1
 
1.9%
161.48 1
 
1.9%
71.83 1
 
1.9%
87.11 1
 
1.9%
34.47 1
 
1.9%
16.57 1
 
1.9%
Other values (35) 35
67.3%
(Missing) 6
 
11.5%
ValueCountFrequency (%)
1.14 1
1.9%
10.51 1
1.9%
10.68 1
1.9%
11.66 1
1.9%
11.99 1
1.9%
16.57 1
1.9%
17.9 2
3.8%
29.53 1
1.9%
31.47 1
1.9%
34.47 1
1.9%
ValueCountFrequency (%)
562.35 1
1.9%
308.95 1
1.9%
280.64 1
1.9%
256.98 1
1.9%
217.19 1
1.9%
183.15 1
1.9%
163.23 1
1.9%
161.48 1
1.9%
158.2 1
1.9%
157.7 1
1.9%

Hydroenergy
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Nuclear
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Firewood
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Sugarcane and products
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)20.0%
Missing47
Missing (%)90.4%
Memory size548.0 B
0.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5
 
9.6%
(Missing) 47
90.4%

Length

2023-07-30T07:45:06.437167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-30T07:45:06.630144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5
100.0%

Most occurring characters

ValueCountFrequency (%)
0 10
66.7%
. 5
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10
66.7%
Other Punctuation 5
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10
100.0%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10
66.7%
. 5
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10
66.7%
. 5
33.3%

Other Primary_x000d_
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing52
Missing (%)100.0%
Memory size548.0 B

Total Primaries
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct50
Distinct (%)100.0%
Missing2
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean347.1474
Minimum91.11
Maximum840.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:45:06.821357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum91.11
5-th percentile141.741
Q1250.6875
median353.535
Q3452.3175
95-th percentile538.9415
Maximum840.4
Range749.29
Interquartile range (IQR)201.63

Descriptive statistics

Standard deviation140.52607
Coefficient of variation (CV)0.40480232
Kurtosis1.6820752
Mean347.1474
Median Absolute Deviation (MAD)101.52
Skewness0.65145204
Sum17357.37
Variance19747.577
MonotonicityNot monotonic
2023-07-30T07:45:07.083919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
173.17 1
 
1.9%
195.56 1
 
1.9%
211.39 1
 
1.9%
331.68 1
 
1.9%
319.61 1
 
1.9%
384.09 1
 
1.9%
360.15 1
 
1.9%
396.87 1
 
1.9%
345.4 1
 
1.9%
152.96 1
 
1.9%
Other values (40) 40
76.9%
(Missing) 2
 
3.8%
ValueCountFrequency (%)
91.11 1
1.9%
124 1
1.9%
132.84 1
1.9%
152.62 1
1.9%
152.96 1
1.9%
173.17 1
1.9%
182.66 1
1.9%
195.56 1
1.9%
211.39 1
1.9%
211.86 1
1.9%
ValueCountFrequency (%)
840.4 1
1.9%
585 1
1.9%
548.9 1
1.9%
526.77 1
1.9%
475.28 1
1.9%
467.94 1
1.9%
466.21 1
1.9%
461.57 1
1.9%
460.89 1
1.9%
459.69 1
1.9%

Electricity
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4286.8538
Minimum520.44
Maximum9299.56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:45:07.349826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum520.44
5-th percentile660.1605
Q11572.1425
median4137.74
Q36670.715
95-th percentile8717.2435
Maximum9299.56
Range8779.12
Interquartile range (IQR)5098.5725

Descriptive statistics

Standard deviation2830.231
Coefficient of variation (CV)0.66021169
Kurtosis-1.2959729
Mean4286.8538
Median Absolute Deviation (MAD)2569.725
Skewness0.275774
Sum222916.4
Variance8010207.7
MonotonicityNot monotonic
2023-07-30T07:45:07.593026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
520.44 1
 
1.9%
578.38 1
 
1.9%
4641.74 1
 
1.9%
5065.44 1
 
1.9%
5296.25 1
 
1.9%
4868.06 1
 
1.9%
4963.94 1
 
1.9%
5164.73 1
 
1.9%
5599.47 1
 
1.9%
5691.06 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
520.44 1
1.9%
578.38 1
1.9%
627.21 1
1.9%
687.12 1
1.9%
721.68 1
1.9%
785.64 1
1.9%
928.69 1
1.9%
1035.89 1
1.9%
1165.95 1
1.9%
1349.14 1
1.9%
ValueCountFrequency (%)
9299.56 1
1.9%
9074.55 1
1.9%
9063.1 1
1.9%
8434.27 1
1.9%
8404.75 1
1.9%
8374.41 1
1.9%
8119.65 1
1.9%
8058.18 1
1.9%
8016.99 1
1.9%
8008.55 1
1.9%

LPG
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)92.9%
Missing38
Missing (%)73.1%
Infinite0
Infinite (%)0.0%
Mean26.705714
Minimum1.25
Maximum57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:45:07.812163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.25
5-th percentile4.396
Q114.725
median30.47
Q335.6775
95-th percentile44.0065
Maximum57
Range55.75
Interquartile range (IQR)20.9525

Descriptive statistics

Standard deviation15.028709
Coefficient of variation (CV)0.56275255
Kurtosis0.024297627
Mean26.705714
Median Absolute Deviation (MAD)6.49
Skewness-0.056428059
Sum373.88
Variance225.86209
MonotonicityNot monotonic
2023-07-30T07:45:08.005680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
30.47 2
 
3.8%
21.58 1
 
1.9%
57 1
 
1.9%
30 1
 
1.9%
31.8 1
 
1.9%
33 1
 
1.9%
1.25 1
 
1.9%
12.44 1
 
1.9%
37.01 1
 
1.9%
36.91 1
 
1.9%
Other values (3) 3
 
5.8%
(Missing) 38
73.1%
ValueCountFrequency (%)
1.25 1
1.9%
6.09 1
1.9%
9.29 1
1.9%
12.44 1
1.9%
21.58 1
1.9%
30 1
1.9%
30.47 2
3.8%
31.8 1
1.9%
33 1
1.9%
36.57 1
1.9%
ValueCountFrequency (%)
57 1
1.9%
37.01 1
1.9%
36.91 1
1.9%
36.57 1
1.9%
33 1
1.9%
31.8 1
1.9%
30.47 2
3.8%
30 1
1.9%
21.58 1
1.9%
12.44 1
1.9%

Gasoline/alcohol
Real number (ℝ)

Distinct51
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.449231
Minimum5.3
Maximum187.54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:45:08.371385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5.3
5-th percentile6.2865
Q145.76
median78.885
Q3120.01
95-th percentile165.5575
Maximum187.54
Range182.24
Interquartile range (IQR)74.25

Descriptive statistics

Standard deviation52.533847
Coefficient of variation (CV)0.66122537
Kurtosis-0.88184264
Mean79.449231
Median Absolute Deviation (MAD)37.105
Skewness0.2116337
Sum4131.36
Variance2759.8051
MonotonicityNot monotonic
2023-07-30T07:45:08.781674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.3 2
 
3.8%
6.27 1
 
1.9%
162.61 1
 
1.9%
119.11 1
 
1.9%
9.08 1
 
1.9%
84.81 1
 
1.9%
187.54 1
 
1.9%
75.62 1
 
1.9%
87.28 1
 
1.9%
45.28 1
 
1.9%
Other values (41) 41
78.8%
ValueCountFrequency (%)
5.3 1
1.9%
5.78 1
1.9%
6.27 1
1.9%
6.3 2
3.8%
6.34 1
1.9%
6.82 1
1.9%
9.08 1
1.9%
13.27 1
1.9%
14.01 1
1.9%
15.57 1
1.9%
ValueCountFrequency (%)
187.54 1
1.9%
180.27 1
1.9%
169.16 1
1.9%
162.61 1
1.9%
157.89 1
1.9%
154.66 1
1.9%
154.22 1
1.9%
137.57 1
1.9%
133.71 1
1.9%
132.25 1
1.9%

Kerosene/jet fuel
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)100.0%
Missing45
Missing (%)86.5%
Infinite0
Infinite (%)0.0%
Mean14.792857
Minimum8.2
Maximum26.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:45:09.138535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum8.2
5-th percentile8.869
Q111.3
median13.04
Q316.385
95-th percentile23.778
Maximum26.94
Range18.74
Interquartile range (IQR)5.085

Descriptive statistics

Standard deviation6.1247523
Coefficient of variation (CV)0.41403444
Kurtosis2.589981
Mean14.792857
Median Absolute Deviation (MAD)3.33
Skewness1.429302
Sum103.55
Variance37.51259
MonotonicityNot monotonic
2023-07-30T07:45:09.512920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
26.94 1
 
1.9%
10.43 1
 
1.9%
12.17 1
 
1.9%
13.04 1
 
1.9%
16.37 1
 
1.9%
16.4 1
 
1.9%
8.2 1
 
1.9%
(Missing) 45
86.5%
ValueCountFrequency (%)
8.2 1
1.9%
10.43 1
1.9%
12.17 1
1.9%
13.04 1
1.9%
16.37 1
1.9%
16.4 1
1.9%
26.94 1
1.9%
ValueCountFrequency (%)
26.94 1
1.9%
16.4 1
1.9%
16.37 1
1.9%
13.04 1
1.9%
12.17 1
1.9%
10.43 1
1.9%
8.2 1
1.9%

Diesel oil
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)83.3%
Missing40
Missing (%)76.9%
Infinite0
Infinite (%)0.0%
Mean41.1725
Minimum3.64
Maximum142.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:45:09.895516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3.64
5-th percentile5.8235
Q18.2475
median19.78
Q343.255
95-th percentile139.7185
Maximum142.76
Range139.12
Interquartile range (IQR)35.0075

Descriptive statistics

Standard deviation49.868159
Coefficient of variation (CV)1.2112007
Kurtosis1.0544678
Mean41.1725
Median Absolute Deviation (MAD)12.17
Skewness1.54487
Sum494.07
Variance2486.8333
MonotonicityNot monotonic
2023-07-30T07:45:10.264861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
13.53 2
 
3.8%
7.61 2
 
3.8%
73.6 1
 
1.9%
26.03 1
 
1.9%
26.93 1
 
1.9%
137.23 1
 
1.9%
142.76 1
 
1.9%
8.46 1
 
1.9%
3.64 1
 
1.9%
33.14 1
 
1.9%
(Missing) 40
76.9%
ValueCountFrequency (%)
3.64 1
1.9%
7.61 2
3.8%
8.46 1
1.9%
13.53 2
3.8%
26.03 1
1.9%
26.93 1
1.9%
33.14 1
1.9%
73.6 1
1.9%
137.23 1
1.9%
142.76 1
1.9%
ValueCountFrequency (%)
142.76 1
1.9%
137.23 1
1.9%
73.6 1
1.9%
33.14 1
1.9%
26.93 1
1.9%
26.03 1
1.9%
13.53 2
3.8%
8.46 1
1.9%
7.61 2
3.8%
3.64 1
1.9%

Fuel oil
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)91.7%
Missing40
Missing (%)76.9%
Infinite0
Infinite (%)0.0%
Mean30.985833
Minimum4.78
Maximum68.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:45:10.611893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4.78
5-th percentile7.4915
Q110.8975
median20.245
Q360.0125
95-th percentile64.884
Maximum68.25
Range63.47
Interquartile range (IQR)49.115

Descriptive statistics

Standard deviation24.263721
Coefficient of variation (CV)0.78305851
Kurtosis-1.5443319
Mean30.985833
Median Absolute Deviation (MAD)10.29
Skewness0.65194444
Sum371.83
Variance588.72814
MonotonicityNot monotonic
2023-07-30T07:45:10.897459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
20.09 2
 
3.8%
20.4 1
 
1.9%
10.2 1
 
1.9%
11.13 1
 
1.9%
62.13 1
 
1.9%
61.67 1
 
1.9%
59.46 1
 
1.9%
9.71 1
 
1.9%
4.78 1
 
1.9%
23.92 1
 
1.9%
(Missing) 40
76.9%
ValueCountFrequency (%)
4.78 1
1.9%
9.71 1
1.9%
10.2 1
1.9%
11.13 1
1.9%
20.09 2
3.8%
20.4 1
1.9%
23.92 1
1.9%
59.46 1
1.9%
61.67 1
1.9%
62.13 1
1.9%
ValueCountFrequency (%)
68.25 1
1.9%
62.13 1
1.9%
61.67 1
1.9%
59.46 1
1.9%
23.92 1
1.9%
20.4 1
1.9%
20.09 2
3.8%
11.13 1
1.9%
10.2 1
1.9%
9.71 1
1.9%

Coke
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct44
Distinct (%)91.7%
Missing4
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean48.859583
Minimum3.47
Maximum151.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:45:11.229391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3.47
5-th percentile5.7515
Q120.2775
median34.205
Q370.14
95-th percentile122.701
Maximum151.74
Range148.27
Interquartile range (IQR)49.8625

Descriptive statistics

Standard deviation39.370773
Coefficient of variation (CV)0.80579429
Kurtosis-0.054637336
Mean48.859583
Median Absolute Deviation (MAD)23.195
Skewness0.93774215
Sum2345.26
Variance1550.0578
MonotonicityNot monotonic
2023-07-30T07:45:11.627172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
41.3 2
 
3.8%
32.61 2
 
3.8%
6.88 2
 
3.8%
10.32 2
 
3.8%
11.7 1
 
1.9%
68.12 1
 
1.9%
25.28 1
 
1.9%
3.47 1
 
1.9%
7.02 1
 
1.9%
21.07 1
 
1.9%
Other values (34) 34
65.4%
(Missing) 4
 
7.7%
ValueCountFrequency (%)
3.47 1
1.9%
4.82 1
1.9%
5.51 1
1.9%
6.2 1
1.9%
6.88 2
3.8%
7.02 1
1.9%
10.32 2
3.8%
11.7 1
1.9%
13.1 1
1.9%
17.9 1
1.9%
ValueCountFrequency (%)
151.74 1
1.9%
135.19 1
1.9%
124.15 1
1.9%
120.01 1
1.9%
115.18 1
1.9%
105.46 1
1.9%
102.68 1
1.9%
96.56 1
1.9%
93.11 1
1.9%
91.73 1
1.9%

Charcoal
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct51
Distinct (%)100.0%
Missing1
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean218.28804
Minimum52.81
Maximum410.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:45:11.943086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum52.81
5-th percentile57.545
Q1144.505
median186.94
Q3319.32
95-th percentile370.01
Maximum410.05
Range357.24
Interquartile range (IQR)174.815

Descriptive statistics

Standard deviation107.77102
Coefficient of variation (CV)0.49371013
Kurtosis-1.20375
Mean218.28804
Median Absolute Deviation (MAD)108.68
Skewness0.045650553
Sum11132.69
Variance11614.592
MonotonicityNot monotonic
2023-07-30T07:45:12.221443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
176.93 1
 
1.9%
201.47 1
 
1.9%
142.67 1
 
1.9%
169.03 1
 
1.9%
184.59 1
 
1.9%
149.93 1
 
1.9%
146.34 1
 
1.9%
172.3 1
 
1.9%
177.44 1
 
1.9%
181.27 1
 
1.9%
Other values (41) 41
78.8%
ValueCountFrequency (%)
52.81 1
1.9%
54.64 1
1.9%
57.23 1
1.9%
57.86 1
1.9%
58.37 1
1.9%
59.01 1
1.9%
59.19 1
1.9%
61.97 1
1.9%
118.81 1
1.9%
119.38 1
1.9%
ValueCountFrequency (%)
410.05 1
1.9%
395.84 1
1.9%
371.3 1
1.9%
368.72 1
1.9%
362.1 1
1.9%
355.8 1
1.9%
352.57 1
1.9%
348.05 1
1.9%
342.89 1
1.9%
340.31 1
1.9%

Gases
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct37
Distinct (%)100.0%
Missing15
Missing (%)28.8%
Infinite0
Infinite (%)0.0%
Mean21.533243
Minimum-0
Maximum33.9
Zeros1
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:45:12.473740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0
5-th percentile7.894
Q115.31
median24.58
Q327.76
95-th percentile31.148
Maximum33.9
Range33.9
Interquartile range (IQR)12.45

Descriptive statistics

Standard deviation8.5392135
Coefficient of variation (CV)0.39655956
Kurtosis-0.31047394
Mean21.533243
Median Absolute Deviation (MAD)4.78
Skewness-0.7896848
Sum796.73
Variance72.918167
MonotonicityNot monotonic
2023-07-30T07:45:12.726550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
18.59 1
 
1.9%
15.19 1
 
1.9%
23.7 1
 
1.9%
29.89 1
 
1.9%
33.9 1
 
1.9%
26.32 1
 
1.9%
27.04 1
 
1.9%
25.59 1
 
1.9%
12.91 1
 
1.9%
11.4 1
 
1.9%
Other values (27) 27
51.9%
(Missing) 15
28.8%
ValueCountFrequency (%)
-0 1
1.9%
5.03 1
1.9%
8.61 1
1.9%
9.03 1
1.9%
9.12 1
1.9%
9.34 1
1.9%
11.4 1
1.9%
12.91 1
1.9%
15.19 1
1.9%
15.31 1
1.9%
ValueCountFrequency (%)
33.9 1
1.9%
31.54 1
1.9%
31.05 1
1.9%
30.5 1
1.9%
29.89 1
1.9%
29.36 1
1.9%
29.18 1
1.9%
28.61 1
1.9%
28.32 1
1.9%
27.76 1
1.9%

Other secondary
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)87.5%
Missing28
Missing (%)53.8%
Infinite0
Infinite (%)0.0%
Mean63.067917
Minimum2.56
Maximum129.27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:45:12.960004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.56
5-th percentile7.9085
Q136.3
median60.5
Q395.79
95-th percentile108.014
Maximum129.27
Range126.71
Interquartile range (IQR)59.49

Descriptive statistics

Standard deviation35.486907
Coefficient of variation (CV)0.56267765
Kurtosis-1.0054125
Mean63.067917
Median Absolute Deviation (MAD)29.955
Skewness-0.12091721
Sum1513.63
Variance1259.3206
MonotonicityNot monotonic
2023-07-30T07:45:13.264683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
95.79 3
 
5.8%
14.36 2
 
3.8%
53.33 1
 
1.9%
6.77 1
 
1.9%
110.15 1
 
1.9%
84.02 1
 
1.9%
129.27 1
 
1.9%
84.41 1
 
1.9%
95.91 1
 
1.9%
89.58 1
 
1.9%
Other values (11) 11
 
21.2%
(Missing) 28
53.8%
ValueCountFrequency (%)
2.56 1
1.9%
6.77 1
1.9%
14.36 2
3.8%
29.67 1
1.9%
35.79 1
1.9%
36.47 1
1.9%
40.16 1
1.9%
45.19 1
1.9%
53.33 1
1.9%
57.66 1
1.9%
ValueCountFrequency (%)
129.27 1
 
1.9%
110.15 1
 
1.9%
95.91 1
 
1.9%
95.81 1
 
1.9%
95.79 3
5.8%
89.58 1
 
1.9%
84.41 1
 
1.9%
84.02 1
 
1.9%
79.79 1
 
1.9%
61.17 1
 
1.9%

Non-energy
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)86.7%
Missing37
Missing (%)71.2%
Infinite0
Infinite (%)0.0%
Mean27.27
Minimum1.07
Maximum107.73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:45:13.475434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.07
5-th percentile3.597
Q112.52
median20.59
Q332.475
95-th percentile76.468
Maximum107.73
Range106.66
Interquartile range (IQR)19.955

Descriptive statistics

Standard deviation27.254348
Coefficient of variation (CV)0.99942605
Kurtosis5.1367574
Mean27.27
Median Absolute Deviation (MAD)11.73
Skewness2.1050871
Sum409.05
Variance742.7995
MonotonicityNot monotonic
2023-07-30T07:45:13.681681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
4.68 2
 
3.8%
36.68 2
 
3.8%
16.87 1
 
1.9%
16.18 1
 
1.9%
17.07 1
 
1.9%
21.25 1
 
1.9%
20.59 1
 
1.9%
8.86 1
 
1.9%
28.27 1
 
1.9%
1.07 1
 
1.9%
Other values (3) 3
 
5.8%
(Missing) 37
71.2%
ValueCountFrequency (%)
1.07 1
1.9%
4.68 2
3.8%
8.86 1
1.9%
16.18 1
1.9%
16.87 1
1.9%
17.07 1
1.9%
20.59 1
1.9%
21.25 1
1.9%
25.37 1
1.9%
28.27 1
1.9%
ValueCountFrequency (%)
107.73 1
1.9%
63.07 1
1.9%
36.68 2
3.8%
28.27 1
1.9%
25.37 1
1.9%
21.25 1
1.9%
20.59 1
1.9%
17.07 1
1.9%
16.87 1
1.9%
16.18 1
1.9%

Total Secundaries
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4703.6233
Minimum756.03
Maximum9456.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:45:13.915488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum756.03
5-th percentile981.722
Q12043.5925
median4730.34
Q37172.8975
95-th percentile8904.4085
Maximum9456.1
Range8700.07
Interquartile range (IQR)5129.305

Descriptive statistics

Standard deviation2754.96
Coefficient of variation (CV)0.58571017
Kurtosis-1.3247071
Mean4703.6233
Median Absolute Deviation (MAD)2696.365
Skewness0.20204906
Sum244588.41
Variance7589804.6
MonotonicityNot monotonic
2023-07-30T07:45:14.161059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
756.03 1
 
1.9%
842.27 1
 
1.9%
5300.26 1
 
1.9%
5503.42 1
 
1.9%
5577.09 1
 
1.9%
5194.63 1
 
1.9%
5397.16 1
 
1.9%
5585.3 1
 
1.9%
5941.32 1
 
1.9%
6058.25 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
756.03 1
1.9%
842.27 1
1.9%
934.18 1
1.9%
1020.62 1
1.9%
1139.79 1
1.9%
1284.31 1
1.9%
1390.63 1
1.9%
1535.14 1
1.9%
1687.23 1
1.9%
1943.15 1
1.9%
ValueCountFrequency (%)
9456.1 1
1.9%
9252.35 1
1.9%
9226.45 1
1.9%
8640.92 1
1.9%
8606.99 1
1.9%
8568.46 1
1.9%
8369.43 1
1.9%
8252.69 1
1.9%
8222.35 1
1.9%
8195.56 1
1.9%

Total
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5037.4187
Minimum929.2
Maximum9715.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size548.0 B
2023-07-30T07:45:14.402163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum929.2
5-th percentile1213.779
Q12510.26
median5131.64
Q37310.6825
95-th percentile9321.3605
Maximum9715.53
Range8786.33
Interquartile range (IQR)4800.4225

Descriptive statistics

Standard deviation2773.4959
Coefficient of variation (CV)0.55057879
Kurtosis-1.2813867
Mean5037.4187
Median Absolute Deviation (MAD)2611.96
Skewness0.2240058
Sum261945.77
Variance7692279.2
MonotonicityNot monotonic
2023-07-30T07:45:14.656631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
929.2 1
 
1.9%
1024.93 1
 
1.9%
5587.54 1
 
1.9%
5714.82 1
 
1.9%
5908.77 1
 
1.9%
5514.23 1
 
1.9%
5781.25 1
 
1.9%
5945.45 1
 
1.9%
6338.2 1
 
1.9%
6403.66 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
929.2 1
1.9%
1024.93 1
1.9%
1146.03 1
1.9%
1269.21 1
1.9%
1458.78 1
1.9%
1630.43 1
1.9%
1756.27 1
1.9%
1942 1
1.9%
2114.85 1
1.9%
2272.93 1
1.9%
ValueCountFrequency (%)
9715.53 1
1.9%
9707.27 1
1.9%
9651.19 1
1.9%
9051.5 1
1.9%
9036.99 1
1.9%
9028.15 1
1.9%
8720.63 1
1.9%
8716.35 1
1.9%
8697.63 1
1.9%
8597.92 1
1.9%

Interactions

2023-07-30T07:44:58.989587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:42.358635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:47.526140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:52.432244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:55.967682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:59.635085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:04.617373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:08.229645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:11.668826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:22.409731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:25.954748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:30.797619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:34.362072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-07-30T07:44:52.561528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:57.371320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:45:01.552645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:45.765369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:51.272771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:54.821427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:58.499881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:02.874120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:07.127426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:10.566526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:21.170069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:24.823717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:29.165237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:33.200923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:36.906815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:40.739360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:45.535951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:49.166060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:52.771749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:57.708084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:45:01.754719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:46.111955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:51.463784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:55.009148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:58.706053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:03.157695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:07.304374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:10.742451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:21.389094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:25.005339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:29.447395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:33.385934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:37.127452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:40.981052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:45.712622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:49.380394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:52.954918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:58.020930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:45:01.967246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:46.394830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:51.652982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:55.196926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:58.886286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:03.461455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:07.498780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:10.929618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:21.607900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:25.194543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:29.679765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:33.593404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:37.334930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:41.299241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:45.929981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:49.575142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:53.140703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:58.258914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:45:02.165433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:46.652745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:51.841464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:55.383356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:59.080593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:03.781420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:07.702240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:11.112447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:21.811792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:25.402221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:30.012984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:33.780770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:37.516350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:41.622524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:46.121668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:49.760393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:53.330694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:58.464617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:45:02.378222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:46.863167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:52.025716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:55.585383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:59.267127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:04.112229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:07.895922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:11.296166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:22.021335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:25.574875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:30.326916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:33.959044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:37.697772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:41.864081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:46.297970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:49.970596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:53.507994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:58.655477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:45:02.529453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:47.187586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:52.230357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:55.780748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:43:59.432000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:04.415100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:08.064193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:11.478634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:22.214367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:25.771200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:30.573562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:34.154778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:37.883935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:42.157174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:46.514723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:50.158650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:53.706419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-30T07:44:58.812163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-30T07:45:14.901457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
YearOilNatural gasCoalTotal PrimariesElectricityLPGGasoline/alcoholKerosene/jet fuelDiesel oilFuel oilCokeCharcoalGasesOther secondaryNon-energyTotal SecundariesTotal
Year1.000-0.0990.732-0.5030.1520.998-0.1670.405-0.286-0.4910.137-0.645-0.818-0.4510.5580.5440.9970.996
Oil-0.0991.000-0.3930.5420.752-0.0970.464-0.009-0.4000.429-0.0710.3430.6460.404-0.643-0.536-0.094-0.082
Natural gas0.732-0.3931.000-0.5840.7060.705-0.368-0.4260.300-0.0270.424-0.336-0.728-0.8850.118-0.0790.7030.714
Coal-0.5030.542-0.5841.0000.247-0.5060.527-0.1590.1070.146-0.2360.6990.6950.547-0.510-0.615-0.506-0.483
Total Primaries0.1520.7520.7060.2471.0000.139-0.041-0.065-0.2500.3720.0740.066-0.0410.2800.072-0.2200.1390.172
Electricity0.998-0.0970.705-0.5060.1391.000-0.0840.403-0.286-0.4910.186-0.650-0.814-0.4580.4730.5190.9990.997
LPG-0.1670.464-0.3680.527-0.041-0.0841.000-0.414-0.300-0.622-0.4510.0540.4880.200-0.515-0.770-0.084-0.128
Gasoline/alcohol0.405-0.009-0.426-0.159-0.0650.403-0.4141.0000.3570.084-0.375-0.386-0.227-0.296-0.1410.4600.4030.395
Kerosene/jet fuel-0.286-0.4000.3000.107-0.250-0.286-0.3000.3571.000-0.3000.4000.4140.0000.1000.1000.543-0.286-0.286
Diesel oil-0.4910.429-0.0270.1460.372-0.491-0.6220.084-0.3001.0000.5770.3810.0070.321-0.493-0.049-0.491-0.491
Fuel oil0.137-0.0710.424-0.2360.0740.186-0.451-0.3750.4000.5771.000-0.058-0.354-0.107-0.0230.1460.1860.137
Coke-0.6450.343-0.3360.6990.066-0.6500.054-0.3860.4140.381-0.0581.0000.7110.546-0.470-0.465-0.649-0.639
Charcoal-0.8180.646-0.7280.695-0.041-0.8140.488-0.2270.0000.007-0.3540.7111.0000.679-0.482-0.356-0.814-0.806
Gases-0.4510.404-0.8850.5470.280-0.4580.200-0.2960.1000.321-0.1070.5460.6791.000-0.3190.405-0.450-0.449
Other secondary0.558-0.6430.118-0.5100.0720.473-0.515-0.1410.100-0.493-0.023-0.470-0.482-0.3191.0000.8040.5220.541
Non-energy0.544-0.536-0.079-0.615-0.2200.519-0.7700.4600.543-0.0490.146-0.465-0.3560.4050.8041.0000.5920.569
Total Secundaries0.997-0.0940.703-0.5060.1390.999-0.0840.403-0.286-0.4910.186-0.649-0.814-0.4500.5220.5921.0000.997
Total0.996-0.0820.714-0.4830.1720.997-0.1280.395-0.286-0.4910.137-0.639-0.806-0.4490.5410.5690.9971.000

Missing values

2023-07-30T07:45:02.871406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-30T07:45:03.537617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-30T07:45:04.024599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

899YearOilNatural gasCoalHydroenergyNuclearFirewoodSugarcane and productsOther Primary_x000d_Total PrimariesElectricityLPGGasoline/alcoholKerosene/jet fuelDiesel oilFuel oilCokeCharcoalGasesOther secondaryNon-energyTotal SecundariesTotal
11970127.54NaN45.63NaNNaNNaNNaNNaN173.17520.44NaN6.27NaNNaNNaN33.80176.9318.59NaNNaN756.03929.20
21971135.19NaN47.48NaNNaNNaNNaNNaN182.66578.38NaN6.34NaNNaNNaN39.31201.4716.76NaNNaN842.271024.93
31972160.92NaN50.93NaNNaNNaNNaNNaN211.86627.21NaN6.82NaNNaNNaN51.73233.1115.31NaNNaN934.181146.03
41973192.59NaN56.00NaNNaNNaNNaNNaN248.59687.12NaN6.30NaNNaNNaN62.08247.3217.80NaNNaN1020.621269.21
51974205.61NaN113.38NaNNaNNaNNaNNaN318.99721.68NaN5.78NaNNaNNaN74.49308.6629.18NaNNaN1139.791458.78
61975223.47NaN122.65NaNNaNNaNNaNNaN346.12785.64NaN5.30NaNNaNNaN93.11368.7231.54NaNNaN1284.311630.43
71976237.53NaN128.11NaNNaNNaNNaNNaN365.64928.69NaN6.30NaNNaNNaN96.56334.4924.59NaNNaN1390.631756.27
81977243.62NaN163.23NaNNaNNaNNaNNaN406.861035.89NaN13.27NaNNaNNaN115.18340.3130.50NaNNaN1535.141942.00
91978271.67NaN155.95NaNNaNNaNNaNNaN427.621165.95NaN22.16NaNNaNNaN120.01348.0531.05NaNNaN1687.232114.85
101979283.06NaN183.15NaNNaNNaNNaNNaN466.211349.14NaN56.13NaNNaNNaN135.19410.0529.36NaNNaN1979.862446.07
899YearOilNatural gasCoalHydroenergyNuclearFirewoodSugarcane and productsOther Primary_x000d_Total PrimariesElectricityLPGGasoline/alcoholKerosene/jet fuelDiesel oilFuel oilCokeCharcoalGasesOther secondaryNon-energyTotal SecundariesTotal
432012NaN335.2611.66NaNNaNNaN0.0NaN346.928119.65NaN104.79NaNNaNNaN6.88123.75NaN14.36NaN8369.438716.35
442013NaN402.35NaNNaNNaNNaNNaNNaN402.358058.18NaN123.02NaNNaNNaNNaNNaNNaN14.36NaN8195.568597.92
452014NaN457.2510.68NaNNaNNaN0.0NaN467.948016.99NaN57.39NaNNaNNaN6.2061.97NaN110.15NaN8252.698720.63
462015NaN463.2911.99NaNNaNNaN0.0NaN475.288008.55NaN54.13NaNNaNNaN5.5158.37NaN95.79NaN8222.358697.63
472016NaN385.5610.51NaNNaNNaNNaNNaN396.078434.27NaN53.23NaNNaNNaN4.8252.81NaN95.79NaN8640.929036.99
482017NaN384.3260.19NaNNaNNaNNaNNaN444.518374.41NaN55.99NaNNaNNaN26.1554.64NaN95.79NaN8606.999051.50
492018NaN386.9872.71NaNNaNNaNNaNNaN459.698404.75NaN72.07NaNNaNNaN34.4157.23NaNNaNNaN8568.469028.15
502019NaN398.4156.52NaNNaNNaNNaNNaN454.929063.10NaN99.26NaNNaNNaN30.9759.01NaNNaNNaN9252.359707.27
512020NaN406.8517.90NaNNaNNaNNaNNaN424.759074.55NaN51.41NaNNaNNaN41.3059.19NaNNaNNaN9226.459651.19
522021NaN241.5417.90NaNNaNNaNNaNNaN259.439299.56NaN57.38NaNNaNNaN41.3057.86NaNNaNNaN9456.109715.53